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In the early April 2021, the tropical cyclone Seroja was formed over the Savu Sea in the southeastern Indonesia. Seroja provided a unique opportunity to observe a tropical cyclone over the Indonesian region using ground-based global navigation satellite systems (GNSS) observations. Precipitable water vapor (PWV) from several permanent GNSS stations in the region was utilized to detect Seroja. From in situ meteorological observations, we found that surface pressure values dropped by more than 14 hPa during Seroja, relative humidity increased, and temperature was reduced. PWV at two nearest stations showed an upward trend (around 70 mm at its peak) during the formation of the cyclone, then dropped immediately (less than 20 mm). After Seroja, the mean PWV was lower (56 mm before and 39 mm after), whereas the standard deviation was higher (5–6 mm before and 9 mm after). We also compared hourly PWV with precipitation from the global satellite mapping of precipitation (GSMaP). Before Seroja, some precipitation events occurred, followed by heavy rains that lasted for several days when the cyclone was passing. After Seroja had passed, both PWV and precipitation dropped significantly. However, while PWV values after Seroja were fluctuating, no rain occurred. We then investigated the water vapor budget to understand the change of PWV over time. We found that precipitation and the divergence of moisture flux played an important role in the change of PWV over time. Heavy precipitation during Seroja resulted in a drop in PWV, although the negative divergence provided a bit of offset. After Seroja had passed, no precipitation occurred, and the change of PWV could be attributed mainly to the moisture divergence. The lagged correlation between PWV and precipitation was determined using moving average over the time series. The highest correlation was found 1–2 days before the event with moving average periods of 7 and 10 days. [ABSTRACT FROM AUTHOR]

GNSS reflectometry (GNSS-R) is a method to derive sea level using Signal to Noise Ratio (SNR) from the Global Navigation Satellite Systems (GNSS). SNR data consist of the direct signal from the satellite (multipath) and of the signals reflected by the sea surface, and hence separating the multipath is necessary to extract the signal from the sea surface. The process of separating multipath may affect the number of data and may eventually affect the quality of the derived sea level values. There are two multipath separation techniques that are mostly used: polynomial fitting and wavelet decomposition. This study investigates the performance of both techniques by applying them to analyze three months of the L1 SNR data of Global Positioning System (GPS) and Globalnaya Navigatsionnaya Sputnikovaya Sistema (GLONASS) as observed from two stations, Barus (CBRS) at North Sumatera from January 1 to March 31, 2022, and Morotai (CMOR) at North Maluku, Indonesia using data from February 1 to May 1, 2022. Comparison with sea level from tide gauge observations shows a high correlation for both techniques, with correlation coefficients of approximately 0.90 and 0.97 for CBRS and CMOR, respectively. The Root Mean Square Error (RMSE) of polynomial fitting for CBRS and CMOR have the same value, 11.5 cm, whereas those of wavelet are 11.4 cm and 11.5 cm. Since polynomial fitting and wavelet decomposition show similar performance, we conclude that both techniques give comparable accuracy of multipath SNR data for GNSS-R in Indonesia with appropriate quality control parameters. [ABSTRACT FROM AUTHOR]